Gene regulatory network inference from sparsely sampled noisy data

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Aalto , A , Viitasaari , L , Ilmonen , P , Mombaerts , L & Goncalves , J 2020 , ' Gene regulatory network inference from sparsely sampled noisy data ' , Nature Communications , vol. 11 , no. 1 , 3493 . https://doi.org/10.1038/s41467-020-17217-1

Title: Gene regulatory network inference from sparsely sampled noisy data
Author: Aalto, Atte; Viitasaari, Lauri; Ilmonen, Pauliina; Mombaerts, Laurent; Goncalves, Jorge
Contributor: University of Helsinki, Department of Mathematics and Statistics
Date: 2020-07-13
Language: eng
Number of pages: 9
Belongs to series: Nature Communications
ISSN: 2041-1723
URI: http://hdl.handle.net/10138/324880
Abstract: The complexity of biological systems is encoded in gene regulatory networks. Unravelling this intricate web is a fundamental step in understanding the mechanisms of life and eventually developing efficient therapies to treat and cure diseases. The major obstacle in inferring gene regulatory networks is the lack of data. While time series data are nowadays widely available, they are typically noisy, with low sampling frequency and overall small number of samples. This paper develops a method called BINGO to specifically deal with these issues. Bench-marked with both real and simulated time-series data covering many different gene regulatory networks, BINGO clearly and consistently outperforms state-of-the-art methods. The novelty of BINGO lies in a nonparametric approach featuring statistical sampling of continuous gene expression profiles. BINGO's superior performance and ease of use, even by non-specialists, make gene regulatory network inference available to any researcher, helping to decipher the complex mechanisms of life.
Subject: PROCESS DYNAMICAL MODELS
BAYESIAN-APPROACH
MCMC METHODS
IDENTIFICATION
111 Mathematics
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